import tensorflow as tf
import numpy as np

# create data
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1 + 0.3

# print(x_data)
# print(y_data)

#学习 w=0.1 b=0.3

#初始化w及b
w = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
print(w)
print(b)

y = w*x_data + b

loss = tf.reduce_mean(tf.square(y-y_data))

print(loss)


optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init)          # Very important

for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(w), sess.run(b))
